Clinical Architecture Healthcare IT Blog

A Primer on the Vocabularies of Meaningful Use - Introduction

I recently had a request to create a post providing a primer on the vocabularies of meaningful use.  Let's start with a review of the vocabularies that are named in the meaningful use criteria described on pages 21 and 22 of the January 13^th release of the federal register located here: http://edocket.access.gpo.gov/2010/pdf/e9-31216.pdf.

The "Chosen Ones"

The listed vocabularies and their purpose are as follows:

Terminology	Stage	Purpose(s)
ICD-9-CM	Stage 1 Stage 1	Problems Procedures
ICD-10-CM	Stage 2	Problems
ICD-10-PCS	Stage 2	Procedures
SNOMED-CT	Stage 1 Stage 2 Stage 2	Problems Problems Lab Results (Submission Public Health)
CPT-4	Stage 1 Stage 2	Procedures Procedures
Third Party Drug Vocabularies*	Stage 1 Stage 1	Medications Electronic Prescribing
RxNorm	Stage 2 Stage 2	Medications Electronic Prescribing
UNII	Stage 2	Medication Allergies
CVX	Stage 1 Stage 2	Immunization Registries Immunization Registries
LOINC	Stage 1 Stage 1 Stage 2 Stage 2	Lab Orders (from Reference labs) Lab Results (from reference labs) Lab Orders (All) Lab Results (All)
UCUM	Stage 2	Units of Measure
CDA template	Stage 2	Vital Signs

* Third Party drug vocabularies that are listed as complete in RxNorm by the NLM

Meaningful Selection

What does it mean that a vocabulary is one of the chosen ones?  My understanding is that the meaningful use criteria (based on my reading of the federal register) defines that to be certified EHR technologies must provide patient summaries and interoperate (exchange data) using the listed vocabularies for their defined purposes.  In other words, the vocabulary standards are for interoperability not native persistence in the EHR application.

It is not reasonable to expect that every hospital / physicians office in the US will migrate their patient data to these standards (and then do it again for 2013).  As a good application architect, your objective is to determine how you will be able to express your client's patient information in the anointed vocabularies.

Where to Learn More

There is a lot I can say about these vocabularies, both their suitability to the task that have been so capriciously assigned to them and the challenges associated in working with each of them.  This is not the post for that particular diatribe.  In this post, I will try to give you some high level information and some places to find out more.  So strap on your learning caps and practice your right click ‘open in new tab' skills.

The next post will cover the problem vocabularies.

Medication Concepts – Engineering Primer – [Part 3] - The Secret Ingredient

When dealing with any terminology domain, to establish a working understanding, you need to get a handle on the anatomy of a term within the domain.  For example, if you are looking at a catalog of automobiles you quickly see a pattern that revolves around the vehicle make, model, production year and other characteristics that identify the vehicle to the required level of granularity.  Regardless of the domain, the pattern typically becomes broken down into primary characteristics, secondary characteristics and modifiers.  The primary characteristic is the core of information that is absolutely essential to the meaning of the term.  In other words, if you began stripping off characteristics the primary characteristics is where you say ‘when’ so that the term is not rendered ambiguous in the domain.  In our automobile example there are, arguably, a couple of primary characteristics: the make and model.  If someone asks you what you drive you typically tell them the make and model (unless the model strongly implies the make or you like bragging about the options package…).  The model year, edition and options are secondary characteristics that further define the vehicle and the color and other minutiae could be considered modifiers (unless it is purple).   

In the medication domain, the primary characteristic is the list of ingredients, more specifically the list of active ingredients.  Active ingredients drive the use of medication concepts and, like with the car example, most people when asked about their medications respond with the active ingredients or the brand name synonym for the active ingredients.  For medications the implied route, dose form, strength are secondary characteristics that are relevant but not always necessary.

The Inactive Ingredients are Inactive… or ARE they?

Active and inactive ingredients typically both live together in the domain of substances (or ingredients).  Whether an ingredient is active or inactive is, in most cases, a role that the ingredient plays as opposed to what the ingredient is.  This post is mostly about active ingredients, but it is worth a few minutes to talk about inactive so that, and an implementer, you understand the conceptual differences and the limitations of the notion of an inactive ingredient when you encounter it in the wild.

The difference between an active and inactive ingredient is subtle to the non-pharmacist.  Typically the active ingredients are the substances that define the medication, while the inactive ingredients are excipients that are introduced in the manufacturing of the drug product OR ubiquitous essence of life ingredients like ‘water’ that do not factor into the medications function.  If you refer to the medication continuum in the previous post, you will note that inactive ingredients do not participate in the abstract or dispensable generalizations.  Since inactive ingredients are, for the most part, introduced by the manufacturing process, any attempt to introduce them into higher level generalizations is risky as it can create false alerts and worse missed alerts (Which is the topic of another post and covered to a small degree in my ‘allergy rule of thumb’ post).

Some may argue that if an inactive ingredient is present in all manufactured forms of a drug you can represent is at a higher level generalization for that particular situation.  I would argue that stretching rules of the composition of a terminology to accommodate a few exceptions is not worth compromising the terminology’s consistency.  You need to know that active ingredients are always active ingredients, diverging from that path leads to the scary woods of unintended consequences. 

You may encounter what looks like an inactive ingredient in an active ingredient list.  This is either: (A) a valid active ingredient in that particular circumstance, (B) introduced because it is clinically relevant and there is no other way for the terminology to deal with this, or (C) it is junk DNA left over from a bygone era.  In any case, you must treat it like an active ingredient: avoid eye contact and sudden movements.  This is discussed more later in this post.

Let’s talk about active ingredients.

The Ingredient Set

Every valid medication concept (I am looking at YOU medical devices…) has one or many active ingredients that make up its primary characteristic.  This may be referred to as an ingredient set, ingredient list, generic drug or the formulation (ingredient set in the medication concept continuum).  In fact, most every drug compendia has a concept that represents this level.  This is important as that defines the set of valid active ingredient combinations. Most, if not all, drug concepts in a medication hierarchy point back to this type of concept.  These ingredient sets break down into a list of individual ingredients.

Base ingredients

A single ingredient can represent a base ingredient or a variation of a base ingredient.  This is significant because a variation of a base ingredient is related to the base ingredient but can have significant differences (which I will not get into here… ask you local pharmacist).  To illustrate this,  consider the following table of RxNorm ingredients that start with ‘Erythromycin’:

RXCUI	SAB	TTY	STR
4053	RXNORM	IN	Erythromycin
4055	RXNORM	IN	Erythromycin Estolate
4056	RXNORM	IN	Erythromycin Ethylsuccinate
24346	RXNORM	IN	Erythromycin Gluceptate
24347	RXNORM	IN	erythromycin lactobionate
24351	RXNORM	IN	erythromycin stearate
236847	RXNORM	IN	ERYTHROMYCIN STINOPRATE

In this list you can see the base ingredient of ‘Erythromycin’ and the variations (or different salt forms of Erithromycin in this example).  In most cases the variations of a base ingredient are clinical equivalent to the base ingredient and add not additional clinical value other than accurately describing the variation of the ingredient in a specific formulation.  Some compendia have only base ingredients, Some have base and variations and some have defined relationships between the variation and the base. 

This information can come into play when processing clinical rules so you need to be aware of it.  For example a clinical rule may only be attached to the base ingredient so you need to use the relationship from the variation to the base ingredient to activate the rule.

In some situation a ingredient variation may represent something other than a salt form of the base.  Here are some examples from RxNorm of non-salt variations:

RXCUI	SAB	TTY	STR
352374	RXNORM	IN	drotrecogin alfa
353106	RXNORM	IN	drotrecogin alfa (activated), lyophilized

  

RXCUI	SAB	TTY	STR
797550	RXNORM	IN	Immune Globulin (Human)
617615	RXNORM	IN	Immune Globulin Subcutaneous (Human)

In some cases there may be no base ingredient – only variations:

RXCUI	SAB	TTY	STR
17609	RXNORM	IN	aluminum acetate
89858	RXNORM	IN	Aluminum carbonate
17610	RXNORM	IN	aluminum chlorhydrate
46241	RXNORM	IN	aluminum chloride
17611	RXNORM	IN	Aluminum chloride hexahydrate
612	RXNORM	IN	Aluminum Hydroxide
81948	RXNORM	IN	Aluminum Hydroxide (Gel), Dried
613	RXNORM	IN	Aluminum Hydroxide Gel
46242	RXNORM	IN	aluminum magnesium hydroxide
615	RXNORM	IN	Aluminum Oxide
17618	RXNORM	IN	aluminum phosphate
54989	RXNORM	IN	aluminum potassium sulfate
543375	RXNORM	IN	Aluminum Sesquichlorohydrate
17621	RXNORM	IN	aluminum sulfate

As an implementer, an awareness of the nature of base ingredients and there variations is useful as it can motivate you to look at the data in different ways, both in terms of development and validation.

When is an Ingredient not an Ingredient?

Every now and then you may encounter an ingredient that is present in an ingredient set that is not an ingredient.  You will recognize this because under certain situations they will wreak havoc.  Sometimes it will be an inactive ingredient, as discussed earlier, and other times it may be a clinical work-around. 

An example could be an ingredient set that has ‘water’ and an active ingredient.  If the user happens to select that drug (either by picking the ingredient set or the brand name synonym) to represent an allergen, they have unwittingly indicated that the patient is allergic to any ingredient set that includes ‘water’. 

Another example of a clinical work-around is a ingredient term that represents a concept like ‘sugar-free’,  ‘alcohol-free’ or ‘Preservative-Free’.  These were introduced to support firing significant clinical alerts without requiring existing terminology users to re-program their applications.  In that respect they are ingenious and likely saved patient’s lives.  The unintended consequence of this, like with the water example, is that if a ingredient set with a ‘freeness’ ingredient is used as an allergen it introduces the notion that the patient is allergic to everything else that is ‘sugar-free’.  There are not many of these but if you encounter one you should make sure that you have exceptions in your allergy checking to ignore ‘free-ness’-based hits.

Finally, some ingredient terminologies may include the notion of a route of administration in an ingredient (see the above example of 'immune globulin' in RxNorm) this is less of an issue because the route is not typically represented as a distinct ingredient, so the net result is similar to a variation of a base ingredient.  Sometimes in these cases the routed ingredient may be disconnected from the base ingredient for clinical reasons.

Ingredients Drive Medication Terminologies

Every use model for medication terminologies is driven by the ingredients.  Take some time, with whatever terminology you have chosen for your implementation, to understand how the ingredients work and how they factor into the decision support modules. Understanding this facet of you medication providers content will provide significant insight into how everything else works.

In the next post of this series we will cover the secondary characteristics of a medication concept.

Medication Concepts - Engineering Primer [Part 2]

The Medication Concept Continuum

In order to understand how a medication concept can be appropriately leveraged, you need to understand its characteristics and which are required to support a particular activity.

For the purposes of this primer, the term ‘medication concept' covers any entity that represents a medication from the ingredient to the physical packaged product.  This excludes therapeutic classes, allergy classes and other taxonomies that may be used to group or relate medication concepts.  This also excludes a medication order, which is an orchestration of a medication concept with other contextual information (we will talk more about this in a later post...).

The following diagram depicts the Medication Concepts Continuum.  It is intended to provide a ‘cheat sheet' that can be referred to throughout the rest of this lesson.  The characteristic breakdown in this diagram are generalizations and, as such, do not represent the actual structure of any existing drug vocabulary vendor.  To interpret how a particular vendors structure fits into this model, please refer to your vocabulary vendor's documentation.

A medication concept falls into one of three generalizations: Abstract, Dispensable or Actual.

The concepts in the Actual generalization represent things that physically exist in the real world.  You can actually put your hands on one.  A tablet or bottle of tablets, for example, is an actual medication concept.

The concepts in the Dispensable generalization represent things that can be conceptually dispensed and administered to a patient.  Another way to think of them is that they represent a completed notion of a medication.  In other words, if I have a dispensable concept I have sufficient information to select a specific actual concept off of the shelf.

The concepts in the Abstract generalization (which is most of them) represent primitive characteristic concepts or an incomplete combination of characteristics.  This type of concept is typically created to function as a navigation pivot OR a an anchor for additional information.  An example of a multi-characteristic abstract concept would be ‘warfarin sodium tablet' (Ingredient + dose form), which is not sufficient to identify an actual physical entity, but it conveys and idea of an ingredient and whether or not it will affect the patient systemically.

The Davinci Code(s)

When you look at the continuum you will also notice that there is the central inverted pyramid (not unlike the La Pyramide Inversée in front of the Louvre) that represents the continuum of medication concepts.  These are the codes that drive order entry, prescribing, alert checking and med-reconciliation. 

On the right side there is a collection of other concepts.  These other concepts are primitives.

You can think of the primitives as the raw building blocks that, when combined, establish the medication concepts that we find in the continuum.  Examples of a primitive would be ‘route of administration', ‘dosage form' and ‘unit of measure'.   For obvious reasons, these are critical to the meaning and stability of the medication concepts.  We cover them in more detail in a future post.

When you examine a drug concept it is important to note where it plugs into the continuum.  In many ways, that will give you an idea as to what you can actually do with it:

• To dispense a medication you need a dispensable
• To determine if a medication systemically affects the patient you need dose form and/or implied route
• To properly validate the dose you need to know the period of release
• To determine the inactive ingredients in a medication you need to know who manufactured it
• To be able to scan it with a bar code reader you may need to know its packaging details.

You may think it sound straight forward, but I have seen attempts to use the wrong concept for the wrong purpose and, like trying to make fruit smoothie with a chipper shredder... it did not end well.

The next topic will be how medication ingredients are typically represented and why it is not always as straightforward as it seems.

Medication Concepts - Engineering Primer [Part One]

Part One - The Medication Domain

As we enter the second decade of the 21^st century, we have been given a mandate to evolve our simplistic, episodic and transient patient records into the robust, longitudinal, and precise paragons of technology that has been promised in board meetings, speeches and science fiction movies.  This can be accomplished.  Like all good architecture, achieving this objective will require an evolution over time that starts with stable foundational concepts that support the goal.  There are a number of domains of clinical terminology: problems, procedures, laboratory tests, nursing orders, etc.  One of the most pervasive and complicated of these is medications.

The purpose of this series on medication concepts for engineers is to provide a overview of the moving parts of medications, how they exist in terminologies today, how they are used in systems today and how they could be used in the future to the betterment of healthcare IT.

Medication concepts are used throughout applications in healthcare information technology in various ways.  They are used to order medications, record allergies, track inventory, manage purchase pricing, identify insurance coverage, transmit prescriptions, trigger alerts and workflow rules, and the list goes on.  It should not be a surprise that the ways that medications are represented in the various standard, proprietary and homegrown terminologies have become quite complicated over the years.

How is the Medication Domain Different?

The medication domain is different from other clinical domains in a few ways.

General functional variability and the resulting ‘Fuzziness'

Unlike a laboratory test or condition, for example, a medication concept can be used to represent something the patient is taking, something I want them to take, something they cannot take (an allergy), something I want to bill for, something I have to pay for and something I have in inventory.  This usage variability, and the overleveraging of existing terminologies to accommodate it, has led to some drug concepts becoming ‘fuzzy' or indistinct as they have been evolved in multiple directions to serve multiple purposes.  This ‘fuzziness' results in many of the "why is it set up like this?' questions that are encountered when an engineer is introduced to the medication domain for the first time.  For example, why do several of the medication terminologies have ‘route' as an attribute of the medication?  To my knowledge there are no medications that have a rectum, so why would a medication have a rectal route?  Why do so few medication terminologies represent bioavailability, when it can have such a significant impact on identifying the right dose? The answers to many of these questions lie in the origins and design drivers behind those concepts.  Medication concepts were used for business transactions before they were used in the clinical setting.  If you do not consider this it is very easy to embrace medication concepts with as number of incorrect assumptions.

Multiple clinical uses and the required attributes

The other difference with the medication domain is the number of clinical decision support functions that can be driven by medication concepts in the delivery of patient care.  Using medication concepts you can check for allergies, check for drug interactions, avoid medication contraindications and verify appropriate dosage levels, just to name a few.   These different uses of medication concepts have different requirements of what characteristics a medication concept must possess in order to drive them correctly.  If you try to drive a particular clinical function with a concept from the wrong level of granularity the result can range from too much clinical advice to no advice or worse, bad advice. 

Multiple levels of granularity

The medication domain is likely the most widely implemented clinical terminology domain in healthcare today.  However, when you take a closer look at how they have been implemented you see that different levels of medication terminologies have been implemented in different ways in these systems.  Medication concepts have been modeled as ingredients, classes, drug and route abstractions, dispensable medications and specific drug products.  In other words, if two applications have implemented medication allergies there is a fairly good chance that they did not do so in the same way or with the same types of medication concepts.

Multiple third party sources

Medication concepts have been around for awhile and were the first concepts used to really drive clinical decision support.  As a result a number of companies have developed stable, updated proprietary medication terminologies and associated clinical content.  This is good as it has provided end user systems with the freedom to stop managing local terminologies and focus on developing sophisticated applications and focus on patient care.  The downside, however, is that these vendors have each created terminologies that are slightly different.  These differences are obvious in some cases and subtle in others.  A misplaced assumption, especially with the subtle differences, can mean the difference in getting a allergy hit or not (or getting a thousand extra allergy hits...)

So... what is a medication concept?  In the next article we will start to examine the characteristics of a medication concepts and how they drive different functionality in healthcare applications.

Mapping SNOMED-CT to ICD9 Screencast

I have completed a new screencast that provides a 17 minute overview on how to leverage the ICD9 cross map files in the SNOMED-CT optional download.  Grab a sandwich, MS Access and curl up by the monitor and see how this great resource from the NLM can save you some time and effort mapping SNOMED-CT terms to ICD9.

Follow this link to the resource page to access the screencast.

I am always looking for new ideas for screencast.  If you have one email me and let me know.

Have a great week!

SNOMED-CT Core Subset – Significant Changes in July File

For those of you evaluating the use of the SNOMED-CT Core Subset, you need to be aware that the NLM has made some non-trivial changes to the format and content of the subset file in the latest (second) release dated 200908 (July).

If you have developed a load program, as we have, that uses the subset file to identify concepts that are included in the subset, it is likely you will need to modify that program.

Here is a summary of the changes:

Term Changes:

• Nine terms were added and eleven terms were retired from the core subset.

New Terms:

SNOMED_CID	SNOMED_FSN	SNOMED_CONCEPT_STATUS
208892001	Closed traumatic dislocation of hip (disorder)	Current
165468009	Erythrocyte sedimentation rate (ESR) raised (finding)	Current
197321007	Steatosis of liver (disorder)	Current
40733004	Infectious disease (disorder)	Current
165346000	Laboratory test result abnormal (situation)	Current
442234001	Serum cholesterol borderline high (finding)	Current
442438000	Influenza due to Influenza A virus (disorder)	Current
442551007	Dental caries extending into dentine (disorder)	Current
4557003	Preinfarction syndrome (disorder)	Current

Retired Terms:

SNOMED_CID	SNOMED_FSN	SNOMED_CONCEPT_STATUS
41006004	Depression (finding)	Ambiguous
309158009	Laboratory finding abnormal (navigational concept)	Current
371330000	Fatty liver (disorder)	Duplicate
131016008	Increased thyroid stimulating hormone level (finding)	Duplicate
166829003	Serum cholesterol borderline (finding)	Ambiguous
191415002	Communicable disease (navigational concept)	Current
78431007	Influenza due to Influenza virus, type A, human (disorder)	Ambiguous
416103000	Elevated erythrocyte sedimentation rate (finding)	Duplicate
50047001	Compound dental caries (disorder)	Ambiguous
63079007	Closed traumatic dislocation of hip joint (disorder)	Duplicate
64333001	Preinfarction angina (disorder)	Duplicate

File Structure Changes:

June Subset	July Subset	Change
SNOMED_CID	SNOMED_CID	-
FSN	SNOMED_FSN	Name Change
CONCEPT_STATUS	SNOMED_CONCEPT_STATUS	Name Change Now uses Description instead of Code!!!
UMLS_CUI	UMLS_CUI	-
OCCURRENCE	OCCURRENCE	-
USAGE	USAGE	-
-	FIRST_IN_SUBSET	New Field (YYYYMM)
IS_RETIRED	IS_RETIRED_FROM_SUBSET	Name Change
-	LAST_IN_SUBSET	New Field (YYYYMM)
-	REPLACED_BY	New Field (SNOMED-CT Concept ID)

New Fields:

New Field	What is it?
FIRST_IN_SUBSET	This is the issue year and month when the concept first appeared in the subset.
LAST_IN_SUBSET	This is the issue year and month when the concept last appeared in the subset as a non-retired concept.
REPLACED_BY	Concept ID of the concept replacing a retired concept.

OUCH!

If you developed a program that loads the core subset file this update likely broke it. 

If you are using a text ODBC/OLEDB driver to load the file the name changes to the columns broke it. 

If you are accessing the fields using sequential access and splitting the fields using the pipe delimiter, the insertion of the FIRST_IN_SUBSET before the IS_RETIRED fields will break your load program.  

If you created a function that uses the coded values in the CONCEPT_STATUS field to support your load logic, that is now broken by the switch to the text value. (I don't understand this change at all.  It seems to run contrary to the move away from free text.  I would change it back...)

Needless to say, this update was a painful one for the early adopter.  But, if you have already created logic based on the inaugural release of the core subset data... and early adopter is what you are and it is not without risks.

Along with the painful changes that left our load program writhing on the ground, clutching its face and yelling "You broke my nose!" are some new useful additions.

The FIRST_IN_SUBSET, LAST_IN_SUBSET and REPLACED_BY_SNOMED_CID are useful lifecycle management fields that will help with the management of term availability.

Patience is a Virtue

If this update frustrated you, I would ask that you focus on the positive and consider that the Core subset is another in a growing line of great, "FREE" work products from our friends at the NLM. 

It is also worth noting that as we in the HIT industry leverage SNOMED-CT, RxNorm and LOINC the bar will continue to be raised in terms of update frequency and format stability.  From the interactions I have had with the NLM, I expect that they are paying attention and will be responsive as we evolve and leverage them more.

Free Advice

As someone who worked at a commercial content provider, I would encourage the following with respect to all data products.

1.) Do not change field/column names lightly if they are included in the file, as developers will leverage that with a text driver to load the information.

2.) Avoid inserting fields into a record, as some load programs will operate based on field order. If you append new fields to the end of the record you will be less likely to disrupt the load.

3.) Coded fields are better than text fields...always.

Regardless of the constructive criticism...this is good stuff.  If we at Clinical Architecture can help you better take advantage of it, give us a call!

Problem Lists: The Cacophony of Concatenation

I have spent some time recently looking into terminologies that are used to represent problem lists.  Specifically the terminologies in question are the current de facto standard ICD-9, its successor ICD-10CM and the big Kahuna SNOMED-CT.

This is a good time to be looking at these, as the healthcare IT industry is starting to evolve electronic patient records (regardless of what acronym you use for it) to be a more useful and accurate picture of the patient's state.  Until recently the patient's problems were (a) not represented in their electronic records, (b) represented using a home grown code that, aside from providing a display name, provided little support for leveragable decision support or (c) represented using an ICD-9CM code.

As I reviewed these terminologies, I ran across something that concerned me.  The concern is with regard to concatenated terms in these terminologies.

What is a Concatenated Term?

A concatenated or combination term is a term that is, in actuality, multiple terms combined together under a single concept identifier.

Here is an example in ICD9CM:

404 - Hypertensive heart and chronic kidney disease

As opposed to the individual single terms:

402 - Hypertensive heart disease

585 - Chronic kidney disease (CKD)

In some cases it can be even more interesting:

404.00 - Hypertensive heart and chronic kidney disease, malignant, without mention of heart failure and with chronic kidney disease stage I through stage IV, or unspecified

When I saw this in the ICD9CM, I assumed it was a legacy issue and existed because ICD9CM has been around for awhile.  So I looked in ICD10-CM...

I132 - Hypertensive heart and chronic kidney disease with heart failure and with stage V chronic kidney disease, or end stage renal disease

And then I looked at SNOMED-CT...

194779001 - Hypertensive heart and renal disease with (congestive) heart failure

So, as it turns out, it is still an ongoing practice to create concatenated terms.  This is not a legacy thing, or it is a legacy thing that has found its way into the terminologies we are supposed to use as we move forward.

I want to stop at this point and remind the reader that I am not a clinical person.  I am a simple country programmer and guerilla informaticist.  So there might be a reason for this I am not seeing.  If there is, I want to know and I will post it here as a follow up.

Here is what was said on the American Health Insurance Management Association (AHIMA) website in an article about ICD10-CM enhancements regarding combination codes.

"ICD-10-CM includes combination codes for conditions and common symptoms or manifestations. A single code may be used to classify two diagnoses, a diagnosis with an associated sign or symptom, or a diagnosis with an associated complication. This allows one code to be assigned, resulting in fewer cases requiring more than one code and reducing sequencing problems.

Coding professionals have encountered sequencing dilemmas when coding conditions such as unstable angina with arteriosclerotic heart disease or diabetes mellitus with a complication/manifestation such as diabetic nephropathy. Manifestation/etiology conditions require two codes with sequencing mandated by ICD-9-CM. Brackets in the index identified that the etiology (diabetes mellitus) code was sequenced before the manifestation (diabetic nephropathy).

In cases when ICD-9-CM did not indicate correct sequencing, coding was not clear-cut, and Coding Clinic advice was needed to sequence conditions appropriately. The ICD-10-CM use of combination codes has greatly simplified this process."

My concern is how we are using terminology and the compromises we need to make to drive a specific purpose. 

In the case of patient problem terminologies, it can boil down to:

(a) Are we trying to solve a sequencing problem to support re-imbursement?

or

(b) Are we trying to allow the computer to assist with the heavy lifting by providing terms that can drive decision support?

Concatenated terms can assist with providing a refined display, with a single click of the patient's complex problem and, apparently, assist with sequencing for coding purposes.  However, this can come at the expense of accurate and fast decision support.

Why do I say this?

When you create a decision support rule in healthcare, very often the rule fires based on the juxtaposition of elements of the patients clinical context.  For example, a drug-drug interaction is an overlap of two drugs that are currently, or about to be, prescribed on a patient's medical record.  Similarly, a contraindication is the overlap of a medication and a patient problem that is known to create a patient safety risk.

By way of example, let's take a fictional medication ‘Nancycillin' and say that it is absolutely contraindicated in patients with Hypertensive heart disease.  If we use ICD9CM, in order to support that rule in our engine, we may setup the following rule:

Nancycillin [is absolutely contraindicated in patients with] (402) Hypertensive heart disease

If I set the above simple rule and the patient has the following in their EMR

Fred Flintstone

Problems:

• Hypertensive Heart Disease (402)
• Hypertensive renal disease, unspecified (403.9)
• Renal Failure (586)

The rule would fire just fine.

However, If I had added instead: 

• Hypertensive heart and renal disease, unspecified, with renal failure (404.92)

The rule would not fire and my patient might have a problem.

To handle the concatenated term we need to setup a collection of rules to cover the potential concatenated terms as follows:

Nancycillin [is absolutely contraindicated in patients with] ...

CODE	Term Description
404.1	Hypertensive heart and renal disease, benign
404.11	Hypertensive heart and renal disease, benign, with heart failure
404.13	Hypertensive heart and renal disease, benign, with heart failure and renal failure
404.12	Hypertensive heart and renal disease, benign, with renal failure
404.10	Hypertensive heart and renal disease, benign, without mention of heart failure or renal failure
404.9	Hypertensive heart and renal disease, unspecified
404.91	Hypertensive heart and renal disease, unspecified, with heart failure
404.93	Hypertensive heart and renal disease, unspecified, with heart failure and renal failure
404.92	Hypertensive heart and renal disease, unspecified, with renal failure
404.90	Hypertensive heart and renal disease, unspecified, without mention of heart failure or renal failure
402	Hypertensive heart disease

This is a potential issue, as the people setting up rules are not always in control of the terminology and I would imagine that next week a new concatenated term with ‘hypertensive heart disease' could be added to the terminology and there would still be a terminological gap.

Relationships to the rescue? 

This is a problem in ICD9-CM and ICD10-CM, what about SNOMED-CT?  SNOMED-CT, as I mentioned, has concatenated terms, but it also has something else... relationships.  Can the relationships in SNOMED-CT bridge this issue?

In the SNOMED-CT relationships table, if you use a concatenated term there is a way to break it into its antecedents through the relationships table using the 'is a' relationship type.

Let's take our SNOMED-CT example of:

Hypertensive heart and renal disease with (congestive) heart failure

It has the following related terms in the SNOMED_CT Relationships Table:

It breaks down the term into parent terms using the ‘Is a' relationship type.  But if you look at the second one, you see that it is also a concatenated term.  When we break that one down we get the following:

It is worth noting that each of these terms also breaks down (or breaks UP) to a parent or parents.  Using the relationship structure when I am done the term ‘Hypertensive heart and renal disease with (congestive) heart failure' results in 72 ‘Is a' antecedents (click here for the full list).

In order to accurately process my rules using the relationship table, I need to traverse the SNOMED-CT relationships across 72 antecedents, for this one term alone.  Keep in mind, this has to be done for every term because I have no way of knowing which terms are concatenated and which ones are single.

So what's missing?

In order to deal with the issues, there are a few things that would come in handy.

Concatenated Term Indicator

A flag on a term that indicates that it is comprised of more than a single concept would provide the savvy terminology consumer with the ability to apply special processing or avoid concatenated terms altogether.

Concatenated Term to Components Relationship

If these terminologies provided relationships to their component terms that would be even better.  This would not be like the ‘is a' relationships in SNOMED-CT.  The ‘Is a' relationship is an ontological relationship that relates concepts into the conceptual framework of SNOMED-CT.  This would be a compositional link relating a concatenated term to the single conceptual terms that, when combined, are the conceptual equal of the concatenated term.  In this relationship structure a concatenated term would never link to another concatenated term.

As an added bonus, if this type of relationship is created it can serve to identify which terms are concatenated. 

This could be done within the existing SNOMED-CT structure by creating a new relationahip type like the 'is a' relationship but based purely on compositional relationships.

If this type of information was available, it could be used when processing clinical decision support to automatically include all concatenated terms that the included term is a component of. 

I am not suggesting this is a simple thing to do, especially when you have terms like the following:

Hypertensive heart and chronic kidney disease, malignant, without mention of heart failure and with chronic kidney disease stage I through stage IV, or unspecified

What I am suggesting is that in order to create targeted, effective clinical decision support there has to be an effective strategy to deal with concatenated terms.  Without that you place a significant burden on the content authors to accurately cover related concatenated terms with a specific rule.  If you decide to rely on the ontological relationships, in the case of SNOMED-CT, you run the risk of generating significant noise as it can't tell where to stop.

My suggestion, in the short term, for those of you looking into using ICD-9, ICD10 CM or SNOMED-CT to drive diagnosis, problem or procedure codes is to create policies that avoid adding concatenated terms into the subset you are providing to users for selection.  This will help you to avoid the problem and prepare your application for a easier road when, in the future, you begin to try to use those codes to drive clinical decision support.

SNOMED-CT Essentials and CORE subset screen cast

I had a request to do a screen cast on the new SNOMED-CT CORE subset.  In order to do it justice, I decided to provide an overview of the SNOMED-CT Essentials terminology and the CORE subset together to provide context for people that are new to both.

Follow this link to check it out.  It is also available on the Resources page.

It is about 20 minutes and I tried a new approach.  I hope you find it useful.

Please do not hesitate to contact me if you have any feedback or suggestions for future screen casts.

SNOMED CT - CORE Subset - Quick Overview and Impressions

I recevied an email from the NLM UMLS Users listserv today with the following subject 'CORE Problem List Subset of SNOMED CT Now Available'.  Being a UMLS enthusiast, I quickly downloaded the data and scoped it out.  I thought I would share what I found out with you.

CORE Problem List Subset, What is it?

It is a subset of the complete SNOMED CT terminology that is design to help implementers by acting as a starter set of codes.

There are 5182 terms in the core data released today as opposed to the roughly 386,000 terms in the complete SNOMED CT terminology.

Where did it come from?

The data released today is based on the datasets submitted by the following institutions:

• Beth Israel Deaconess Medical Center
• Intermountain Healthcare
• Kaiser Permanente
• Mayo Clinic
• Nebraska University Medical Center
• Regenstrief Institute
• Hong Kong Hospital Authority

Why was it created?

This new core subset can provide a vendor or institution with a starter set of common terms that are used to record clinical observations (in fact CORE stands for Clinical Observations Recording and Encoding).

What is in the file that you can download?

The terms that were selected are available in a pipe ‘|' delimited file with the following record format.

 

Position	Field name	Description
1	SNOMED_CID	This is the concept identifier for the term.  (If you are a regular SNOMED enthusiast, this is the same ID that you would find in the SCT_CONCEPTS_yyyymmdd.txt file.)
2	FSN	Fully Specified Name (the term description)
3	CONCEPT_STATUS	This is the concept status of the concept ID in the SCT_CONCEPT file.  According to the extractions rules for the core this should always be zero, which means ‘current'.  (Which also means you can probably ignore this field).
4	UMLS_CUI	Concept Unique Identifier for this SNOMED CT concept in the UMLS Metathesaurus MRCONSO table.
5	OCCURRENCE	The number of contributing institutions that have the concept in their problem list (currently from 1-7).
6	USAGE	The sum of the usage of this term divided by the 7.  (I wonder if this would be better if it was the sum of the usage divided by the occurrence?? - I will follow up with NLM.)
7	IS_RETIRED	This is a field for the future to support when terms are retired.  I would assume that the CONCEPT_STATUS field would also reflect that the SNOMED CT concept is no longer current as well.

Note:  I went back and forth on the USAGE field.  I thought it was interesting that the sum of the usage was divided by the full count of seven and not the OCCURRENCE value.  When you take the USAGE number, multiply it by seven and divide by the OCCURRENCE number the result is, in most cases, a much higher value that reflects the usage of the term within the institutions that are actually using the term.  If you are a big data nerd (like me) the variance in how the terms are ranked depending on which way you look at the usage is interesting.  I am also interested on how the original institutional average was calculated. (once again... nerd).

A Quick Look at the Data

When you take the supplied terms and sort them in order based on the USAGE number, here are the top 25 terms.

When I see this list it seems reasonable to me that these would have a higher usage in a problem or finding list.  All of the terms are at a fairly high level and are the types of things you would expect to have a higher volume of occurances. 

Impressions

If you are just getting started with SNOMED CT and thinking about using it as a reference terminology for tracking findings and problems in your electronic medical record, this new CORE subset is a great starting point.  Kudos to the NLM and the constributing institutions for providing this information - it should facilitate the implementation of SNOMED CT by providing a place to start.

For more information checkout the full write up on the NLM website at:

http://www.nlm.nih.gov/research/umls/Snomed/core_subset.html

 

Basic Interoperability with RxNorm

As we here at Clinical Architecture have been developing our Symedical product, I have had the pleasure of spending some quality time with UMLS Metathesaurus and RxNorm.  As I was going through this journey of discovery, I thought to myself that it might be a good idea to share my findings and experiences with others who might also be looking into using RxNorm and UMLS to enhance or improve their clinical interoperability.

So, to that end, I have created the first of, in what I hope will be, a series of Screen Casts that provide some insight into UMLS and RxNorm.

(Video should work in all browsers but requires the Quicktime plug-in)

Basic Clinical Interoperability with RxNorm - Screencast

Special thanks Jan Willis and the other folks at NLM for their feedback.